The document discusses the evolution of clinical trials using real-world evidence to enhance product value, focusing on regulatory perspectives and feasibility analysis for effective patient enrollment. It emphasizes the importance of incorporating advanced analytics and AI technologies for optimizing trial operations and improving outcomes. Key points include the need for relaxed eligibility criteria to enhance trial participation and foster medical innovation across various disciplines.

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1
DayOne - Next generation Clinical Trials
Stijn Rogiers, Principal Industry Consultant
SAS Global Health and Life Sciences Practice
June 25th 2019

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Twitter: @StijnRogiers
LinkedIn: https://www.linkedin.com/in/stijnrogiers/
Email: stijn.rogiers@sas.com
Introduction

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Real World Evidence
“Maximize product value beyond efficacy and safety”
Society
Patient
Provider
Clinical
Economic
Payer/HTA
Comparative
effectiveness
Cost-utility
Budget Impact
Long-term
Outcomes
Direct/Indirect Cost
Cost:Outcomes
Cost Consequences
Resource Utilization
QALY
Equal access to healthcare
Return to work
Caregiver burden
Productivity
Morbidity, Mortality
Symptoms
Functionality
Preferences
Quality of Life
Effectiveness
Tolerability
Safety
Compliance
Adherence
ConvenienceEfficacy
Safety
Morbidity
Mortality
Case Study: Maximize Product Value with Real World Evidence, Maria Kubin, VP Bayer Healthcare –Eye for Pharma

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Regulatory Perspective (US FDA)
Framework for FDA's Real World Evidence Program (Dec 2018)

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PRO
EMR
CLAIMS
LABS
RX
REGISTRY
COA
SAFETY
MORTALITY
DIGITAL
mHEALTH
SOCIAL MEDIA
CONSUMER
Patient
Recruitment
Trial
Optimization
Synthetic
Control Arms
BOI
Drug
Utilization
PASS
Pragmatic
Trial
Natural
Hx
Risk /
Benefit
Disease
Mechanism
Comorbidities
Value
Prop
Patient
Journey
Economic
Model
Comparative
Effectiveness
Outcome
Predictions
Patient
Journey
Epidemiology
Pharmacovigilance
Regulatory
Affairs
R&D
Medical Affairs
HEOR
Market Access
HTA
Commercial

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Feasibility Analysis
How can sponsors accurately measure the feasibility of a protocol and plan accordingly to
achieve timely and cost-effective enrollment and the quality data to advance their pipelines?
 True clinical trial feasibility includes strategic, scientific, operational, and patient recruitment considerations
 Traditional approach in our industry
 Historical Data / Past Experiences, Previous Studies (CRO, Pharma)
 Discussions with potential sites and Patient Organizations
 Educated guesses
 Focus is on the enrollability of a clinical trial

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Cohort Generation
“What if scenario(s)” … What if we loosen exclusion criteria
https://www.statnews.com/2018/04/05/clinical-trials-excluding-patients/
Running clinical trials effectively and efficiently is critical to medical progress, not
just in cancer care but across disciplines. Allowing more patients to be enrolled in
trials will speed the medical innovation process, allow more sick people to access
potentially beneficial therapies, and produce more generalized results. Spurred by
this month’s FDA meeting, lead researchers, sponsors, and regulators must rethink
their approach to clinical trial eligibility (5 Apr 2018).

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Add-ins
Cohort Characterization Reports

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Add-ins
Use SAS (industry) / Develop company specifics

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Clinical Trial Operational Analytics
Enrollment Optimization: State / Site Selection
State
CA
Fl
IL
MI
NY

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Clinical Trial Operational Analytics
Enrollment Optimization: Stringent Vs. Relaxed
Stringent Scenario
Relaxed Scenario

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Generate additional insights
Data Mining & Machine Learning – Model Comparison

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A Study showing real world use of ‘real world data’
http://theoncologist.alphamedpress.org/content/early/2018/12/27/theoncologist.2018-
0307.short?rss=1&related-urls=yes&legid=theoncologist;theoncologist.2018-0307v1
https://datavant.com/2019/01/14/a-study-showing-real-world-use-of-real-world-data/

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AI and medicine

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Results
An in-depth scan
has a lot of data and the
outcomes can be
improved with the use of
advanced analytics on
patients’ health data and
history.
“
”Prof. Dr. Geert Kazemier
Cancer Center Amsterdam, aUMC

19. Automatic Segmentation

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Precision medicineAI and medicine
Kim et al., Nature Biotechnology, 37, 389-406 (2019)

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ASSIST’s Always-on Wearable Platforms
Long Term Monitoring of health and environment
Correlation of multiple sensors signals
Clinical studies in various health domains
• Self-powered
• Physiological, biochemical
and environmental sensors
• Wearable, wireless and
comfortable
• Informative and continuous
data
25

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Pricing and cost Ethical rulesAI/ML regulation Data TransparencyPersonal data
protection

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AI IN Health Care and Life Sciences
OPERATIONAL
EFFICIENCY
CLINICAL SCIENCE
 Reducing physician burnout
 Meaningfully connect different data sources
 Financial impact, cost, fraud and abuse
 Workflow optimization (simulation, DL)
 Clinical trial operations
 Manufacturing quality
REGULATION AND
GOVERNANCE
Efficiency increase of health
care and clinical development
Faster access to care and
support for physician
Risk, governance, auditability
 Deep learning for computer vision and
biological signals
 IoMT : wearables, devices and modern
clinical trials
 Patient engagement : chatbots
 RWE – cohort analysis – matching
patients,
 Ethical AI
 Enterprise implementation and
repeatability
 Data privacy
 Traceability